class_docstr: operations performed:
    1x1 x depth
    3x3 x depth dilation 6
    3x3 x depth dilation 12
    3x3 x depth dilation 18
    image pooling
    concatenate all together
    Final 1x1 conv
code: def forward(self, x, edge):
      x_size = x.size()
  
      img_features = self.img_pooling(x)
      img_features = self.img_conv(img_features)
      img_features = F.interpolate(
          img_features, x_size[2:], mode="bilinear", align_corners=True
      )
      out = img_features
  
      edge_features = F.interpolate(
          edge, x_size[2:], mode="bilinear", align_corners=True
      )
      edge_features = self.edge_conv(edge_features)
      out = torch.cat((out, edge_features), 1)
  
      for f in self.features:
          y = f(x)
          out = torch.cat((out, y), 1)
      return out
  
imports: import torch
  import torch.nn.functional
  from torch import nn
  from .None import Resnet
  from .wider_resnet import wider_resnet38_a2
  from .mynn import initialize_weights, Norm2d
  from torch.autograd import Variable
  from .my_functionals import GatedSpatialConv
  import cv2
  import numpy
label_desc: Use image node_id 2 for calling the _AtrousSpatialPyramidPoolingModule obj's underlying member method code with example usage: obj.forward(x, edge) and returns: out